scPipe report for sample RPI-9

Parameters for each preprocessing step

Parameters for sc_trim_barcode

File paths

• input fastq1: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.R2.fastq.gz
• input fastq2: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.R1.fastq.gz
• output fastq: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.combined.fastq.gz

Read structure

assume read1 contains the transcript

• barcode in read1: NA
• barcode in read2: start at position 6, length 7
• UMI in read2: start at position 0, length 6

Read filter

• remove reads that have N in its barcode or UMI: FALSE
• remove reads with low quality: TRUE
  • minimum read quality: 20
  • maximum number of base below minimum read quality: 2

Parameters for alignment

• input fastq: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.combined.fastq.gz
• output bam file: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.combined.subread.bam
• genome index: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/GRCh38.p13/GRCh38.p13_with_ERCC

Parameters for sc_exon_mapping

• input bam file: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.combined.subread.bam
• output bam file: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.combined.exon.bam
• transctiptome annotations: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/GRCh38.p13/gencode.v35.primary_assembly.annotation.gff3, /stornext/Home/data/allstaff/h/hickey/.local/share/renv/cache/v5/R-4.0/x86_64-pc-linux-gnu/scPipe/1.12.0/59e3de64e19be41dfc11976d6a8db4d7/scPipe/extdata/ERCC92_anno.gff3
• do strand specific mapping: TRUE
• fix chromosome names: FALSE

Parameters for sc_demultiplex

• input bam file: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.combined.exon.bam
• output folder: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9
• barcode annotation file: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.barcode_annotation.csv
• maximum mismatch allowed in barcode: 1

Parameters for sc_gene_counting

• output folder: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9
• barcode annotation file: /stornext/Projects/score/Analyses/C094_Pellicci/extdata/NN227/scPipe/RPI-9/RPI-9.barcode_annotation.csv
• UMI correction: simple correction and merge UMI with distance 1
• gene filtering: FALSE

Data summary

The organism is “hsapiens_gene_ensembl”, and gene id type is “ensembl_gene_id”.

Overall barcode statistics

if (is.null(params$organism) || is.na(params$organism)) {
  sce = create_sce_by_dir(params$outdir)
} else {
  sce = create_sce_by_dir(params$outdir, organism=params$organism, gene_id_type=params$gene_id_type)
}
overall_stat = demultiplex_info(sce)
datatable(overall_stat, width=800)

Plot barcode match statistics in pie chart:

plot_demultiplex(sce)

Read alignment statistics

ggplotly(plot_mapping(sce, dataname=params$samplename, percentage = FALSE))

ggplotly(plot_mapping(sce, dataname=params$samplename, percentage = TRUE))

Summary and distributions of QC metrics

if (any(colSums(counts(sce)) == 0)) {
  zero_cells = sum(colSums(counts(sce)) == 0)
  sce = sce[, colSums(counts(sce)) > 0]
} else {
  zero_cells = 0
}

Datatable of all QC metrics:

sce = calculate_QC_metrics(sce)

cannot connect to the ensembl database. ERROR:
Error in useMart("ensembl"): could not find function "useMart"

Try to use org.Hs.eg.db.

'select()' returned 1:many mapping between keys and columns

cannot connect to the ensembl database. ERROR:
Error in useMart("ensembl"): could not find function "useMart"

Try to use org.Hs.eg.db.
'select()' returned 1:many mapping between keys and columns

if(!all(colSums(as.data.frame(QC_metrics(sce)))>0)){
  QC_metrics(sce) = QC_metrics(sce)[, colSums(as.data.frame(QC_metrics(sce)))>0]
}
datatable(as.data.frame(QC_metrics(sce)), width=800, options=list(scrollX= TRUE))

Summary of all QC metrics:

datatable(do.call(cbind, lapply(QC_metrics(sce), summary)), width=800, options=list(scrollX= TRUE))

Number of reads mapped to exon before UMI deduplication VS number of genes detected:

ggplotly(ggplot(as.data.frame(QC_metrics(sce)), aes(x=mapped_to_exon, y=number_of_genes))+geom_point(alpha=0.8))

knitr::knit_exit()